In [1]:
import numpy as np
import pandas as pd
import sklearn
import gc
import functions
import scipy
In [13]:
data        = pd.read_csv('/Users/pwangel/Downloads/pluripotent_atlas_data.tsv', sep='\t', index_col=0)
annotations = pd.read_csv('/Users/pwangel/Downloads/pluripotent_annotations.tsv', sep='\t', index_col=0)
genes       = pd.read_csv('/Users/pwangel/Downloads/pluripotent_atlas_genes.tsv', sep='\t', index_col=0)

annotations['display_metadata'] = annotations.generic_sample_type
In [3]:
data = functions.transform_to_percentile(data)
In [4]:
#genes = functions.calculate_platform_dependence(data, annotations)
genes = pd.read_csv('/Users/pwangel/Downloads/pluripotent_atlas_genes.tsv', sep='\t')
In [5]:
pca        = sklearn.decomposition.PCA(n_components=10, svd_solver='full')
pca.fit(functions.transform_to_percentile(data.loc[genes.Platform_VarFraction.values<=0.25]).transpose())
pca_coords = pca.transform(functions.transform_to_percentile(data.loc[genes.Platform_VarFraction.values<=0.25]).transpose())
In [14]:
functions.plot_pca(pca_coords, annotations,pca, \
                   labels=['generic_sample_type', 'Platform_Category', 'Dataset'], colour_dict={})
In [8]:
annotations.columns
Out[8]:
Index(['Dataset', 'dataset_name', 'chip_id', 'replicate_group_id', 'organism',
       'sample_type', 'generic_sample_type', 'final_cell_type',
       'parental_cell_type', 'sex', 'labelling', 'name', 'description',
       'number of samples', 'sample types', 'platform', 'project', 'species',
       'Detection threshold', 'Platform_Category', 'Platform_Colour',
       'Platform_Symbol', 'Handle'],
      dtype='object')
In [ ]: